## pyspark.sql.functions.pandas_udf

PySpark’s PandasUDFType is a type of user-defined function (UDF) that allows you to use the power of Pandas library with PySpark dataframes. It allows you to define a UDF that takes one or more columns of a PySpark dataframe as input and returns a Pandas dataframe as output.

For example, you can use a PandasUDFType to calculate the moving average of a column in a PySpark dataframe as follows:

```
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import DoubleType
# Define the UDF
@pandas_udf(DoubleType())
def moving_average(values, window):
return values.rolling(window).mean()
# Use the UDF in a PySpark dataframe
df = spark.createDataFrame([(1, 2, 3, 4), (4, 5, 6, 7), (7, 8, 9, 10)], ["col1", "col2", "col3", "col4"])
df.select("col1", moving_average("col1", 2).over(Window.rowsBetween(-1, 1)).alias("moving_avg")).show()
```

In this example, the UDF **moving_average** takes two arguments: **values** and **window**. **values** is the column of the PySpark dataframe that we want to calculate the moving average of, and **window** is the window size for the moving average. The UDF then returns the rolling mean of **values** with the given window size. The UDF is then used in a PySpark dataframe **df** with window size 2 to calculate the moving average of column col1 and it returns the rolling mean of col1 with window size 2.

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